Risk assessment and remedial policy evaluation using predictive modeling
- Harvard Univ., Cambridge, MA (United States)
- Univ. of Pittsburgh, PA (United States)
As a result of nuclear industry operation and accidents, large areas of natural ecosystems have been contaminated by radionuclides and toxic metals. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions, not only economic costs but also human and environmental risk assessments are desired. This paper introduces a general framework for risk assessment and remedial policy evaluation using predictive modeling. Ecological risk assessment requires evaluation of the radionuclide distribution in ecosystems. The FORESTPATH model is used for predicting the radionuclide fate in forest compartments after deposition as well as for evaluating the efficiency of remedial policies. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer annually. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus and a better methodology is required. In conclusion, FORESTPATH modeling framework could have wide applications in environmental remediation of radionuclides and toxic metals as well as in dose reconstruction and, risk-assessment.
- OSTI ID:
- 393933
- Report Number(s):
- CONF-9607135-; ISSN 0017-9078; TRN: 96:028614
- Journal Information:
- Health Physics, Vol. 70, Issue Suppl.6; Conference: 41. Annual Meeting of the Health Physics Society, Seattle, WA (United States), 21-25 Jul 1996; Other Information: PBD: Jun 1996
- Country of Publication:
- United States
- Language:
- English
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